est_r0_mle

Estimates the basic reproduction number (R0) using maximum likelihood and a parametric
model based on generation time and incidence data.

Provides various functions for parameter estimation of one-dimensional
stable distributions and their mixtures. It implements a diverse set of
estimation methods, including quantile-based approaches, regression methods
based on the empirical characteristic function (empirical, kernel, and
recursive), and maximum likelihood estimation. For mixture models, it provides
stochastic expectation–maximization (SEM) algorithms and Bayesian estimation
methods using sampling and importance sampling to overcome the long burn-in
period of Markov Chain Monte Carlo (MCMC) strategies. The package also includes
tools and statistical tests for analyzing whether a dataset follows a stable
distribution. Some of the implemented methods are described in
Hajjaji, O., Manou-Abi, S. M., and Slaoui, Y. (2024) <doi:10.1080/02664763.2024.2434627>.

Solym Manou-Abi

MixStable

Parameter Estimation for Stable Distributions and Their Mixtures

Adam Najib

Yousri Slaoui

est_r0_mle function

<dl><dt>incidence</dt>
<dd>Numeric vector of incidence counts.</dd>
<dt>gen_time</dt>
<dd>Mean generation time.</dd></dl>

Arguments

MLE estimation of R0 using generation time — est_r0_mle

<dl>

<dt>incidence</dt>
<dd>Numeric vector of incidence counts.</dd>


<dt>gen_time</dt>
<dd>Mean generation time.</dd>

</dl>

est_r0_mle: MLE estimation of R0 using generation time

Description

Usage

Value

Arguments